Ensemble of clustered dual-stage attention-based recurrent neural networks for multivariate time series prediction

Abstract

A method for multivariate time series prediction is provided. Each time series from among a batch of multiple driving time series and a target time series is decomposed into a raw component, a shape component, and a trend component. For each decomposed component, select a driving time series relevant thereto from the batch and obtain hidden features of the selected driving time series, by applying the batch to an input attention-based encoder of an Ensemble of Clustered dual-stage attention-based Recurrent Neural Networks (EC-DARNNS). Automatically cluster the hidden features in a hidden space using a temporal attention-based decoder of the EC-DARNNS. Each Clustered dual-stage attention-based RNN in the Ensemble is dedicated and applied to a respective one of the decomposed components. Predict a respective value of one or more future time steps for the target series based on respective prediction outputs for each of the decomposed components by the EC-DARNNS.